1. Research question

Labor shortage is currently significant challenge in global agriculture, while both land use and labor is affecting agricultural food supply. In this report, we aim to analyze the trend of agricultural land use and labor from 1961 to 2019 in 16 key agricultural countries, investigating the relationship between labor issues in agriculture and land use to understand their interdependencies and effects on global agriculture.

2. Data set introduction

The dataset comprises agricultural land use, labor and food supply from 1961 to 2019 for 16 major agricultural production countries, providing insights into global agricultural trends and practices.The data sets with 944 observations and 6 variables were extracted from Our World in Data [https://ourworldindata.org/grapher/agricultural-labor-land]. It is an open-source database and can be used for research and analysis purposes.

The extracted dataset has variables from countries including Australia, Brazil, Canada, China, France, Germany, India, Mexico, Netherlands, New Zealand, Russia, South Africa, South Korea, Turkey, UK, USA from 1961 to 2019. There are three numerical variables– agricultural land use for the sum of croplands and permanent pastures for livestock grazing, agricultural labor for the number of people in agriculture, which includes hiredlabor and unpaid family labor, and agricultural food supply for the total output of agricultural products.

3. Data set description

#Import the dataset.
data <- read.csv("agricultural_labor_land.csv")

data1 <- data.frame(data)

The dataset contains 6 variables and 944 observations. The figure of the code is showing as the following.

# Display the screenshot of code
knitr::include_graphics("Image/Screenshot.png")

# Create a table with variable names for data1
variable_names <- data.frame(Variable = names(data1))

kable(variable_names, caption = "Variable Names of dataset")
Variable Names of dataset
Variable
Entity
Code
Year
ag_land_index
labor_index
food_supply_per_capita 
str(head(data1, 2))
## 'data.frame':    2 obs. of  6 variables:
##  $ Entity                : chr  "Australia" "Australia"
##  $ Code                  : chr  "AUS" "AUS"
##  $ Year                  : int  1961 1962
##  $ ag_land_index         : num  91.9 93.9
##  $ labor_index           : num  140 140
##  $ food_supply_per_capita: num  1.36 1.67

4. Data Summary

# Select 2 numerial variables and one character, calculate two summary statistics
summary_stats <- data1 %>%
  select(Entity, Year, ag_land_index,labor_index) %>%
  group_by(Entity) %>%
  summarise(
    mean_land_use = mean(ag_land_index, na.rm = TRUE),
    variance_land_use = var(ag_land_index, na.rm = TRUE),
    mean_labor = mean(labor_index, na.rm = TRUE),
    variance_labor = var(labor_index, na.rm = TRUE),
  )

kable(head(summary_stats, 10))
Entity mean_land_use variance_land_use mean_labor variance_labor
Australia 100.10170 13.886922 125.54146 170.23519
Brazil 80.48894 283.270685 132.93399 325.02212
Canada 102.12398 13.154989 152.13008 1870.95271
China 90.94096 46.200156 134.58977 607.19428
France 99.47961 6.851704 231.63034 18205.34443
Germany 102.02014 5.943474 295.73544 31186.82249
India 87.12817 95.128728 91.31982 268.88501
Mexico 90.89976 110.093270 87.12339 133.00203
Netherlands 89.83478 48.221104 152.92678 1853.03063
New Zealand 147.89999 1345.215344 102.38662 91.59432

From this data summary, it can be observed that Germany and France have the highest mean labor values, suggesting a significant workforce in agriculture, with Germany also exhibiting the highest variance in labor, indicating large fluctuations or diversity in agricultural labor over the observed period. Additionally, New Zealand stands out with a notably high mean land use and the greatest variance in land use, which could imply extensive and varied agricultural practices in terms of land utilization.

5. Visualisations

# Create one figure related with research question.
long_data <- data1 %>%
  gather(key = "Type", value = "Value", ag_land_index, labor_index)

aggregated_data <- long_data %>%
  group_by(Year, Type) %>%
  summarise(Total = sum(Value, na.rm = TRUE))
## `summarise()` has grouped output by 'Year'. You can override using the
## `.groups` argument.
ggplot_object <- ggplot(aggregated_data, aes(x = Year, y = Total, color = Type)) +
  geom_point() +
  scale_x_continuous(breaks = seq(min(aggregated_data$Year), max(aggregated_data$Year), by = 5)) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        legend.position = "bottom") + 
  labs(title = "Total Land Use vs. Total Labor of Main Agricultural Countries",
       x = "Year",
       y = "Total Value")

# Convert to an interactive plot
interactive_plot <- ggplotly(ggplot_object)

# Display the plot
interactive_plot

Figure 1: Total Land Use vs. Total Labor of Main Agricultural Countries from 1961 to 2019 

# Add additional figure and table
# Agricultural Labor over 16 countries from 1961 to 2019
p <- ggplot(data1, aes(x = Year, y = labor_index, color = Entity)) +
  geom_point() +
  theme_minimal() +
  labs(title = "Scatter Plot for agricultural labor over 16 countries", 
       x = "Year", 
       y = "Labor")

# Convert to a plotly interactive object
p_interactive <- ggplotly(p, tooltip = c("x", "color"))

# Show the interactive plot
p_interactive

Figure 2: Agricutural Labor over 16 countries from 1961 to 2019 

# Use table to describing the data
# Summarize data by entity
summarized_data <- data1 %>%
  group_by(Entity) %>%
  summarise(
    mean_labor = mean(labor_index, na.rm = TRUE),
    mean_land_use = mean(ag_land_index, na.rm = TRUE),
    mean_food_supply = mean(food_supply_per_capita, na.rm = TRUE)
  )

# Create a table with kable
kable(summarized_data, caption = "Labor, Land Use, and Food Supply by Entity")
Labor, Land Use, and Food Supply by Entity
Entity mean_labor mean_land_use mean_food_supply
Australia 125.54146 100.10170 3.815671
Brazil 132.93399 80.48894 23.065022
Canada 152.13008 102.12398 9.242143
China 134.58977 90.94096 5.164526
France 231.63034 99.47961 8.833132
Germany 295.73544 102.02014 6.802649
India 91.31982 87.12817 7.276023
Mexico 87.12339 90.89976 125.825708
Netherlands 152.92678 89.83478 2.775343
New Zealand 102.38662 147.89999 2.330976
Russia 184.48464 105.25769 0.502203
South Africa 128.68122 101.63561 107.369080
South Korea 240.43667 119.06752 9.407233
Turkey 93.69406 99.55939 14.099609
United Kingdom 157.05209 108.34152 3.015138
United States 122.41994 106.93250 10.817130

6. Conclusions

The analysis of agricultural data for major agricultural countries reveals significant trends in land use and labor. The data shows a steady increase in land use until 1990, followed by a period of fluctuation and a slight decrease, alongside a consistent decline in labor. This suggests a shift towards more efficient, mechanized farming, driven by technological, economic, and policy changes. The substantial labor decrease, despite varying land use trends, indicates a workforce restructuring, potentially due to urban migration and demographic shifts.

Moreover, country-specific data reveals diverse agricultural dynamics. While Germany, France, and South Korea experience significant labor declines, countries like Russia, UK, and US show gradual decreases. In contrast, labor in China and India initially increased before declining, and Mexico shows a consistent rise. These variations suggest differing agricultural practices and levels of mechanization, reflecting a complex agricultural landscape shaped by a multitude of global and local factors.